This two part tutorial on Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) imagery focuses on two areas of research: (1) SAR ATR using fully polarimetric data and (2) SAR ATR using super-resolution image processing. In part 1, a polarimetric Ka-Band SAR sensor is described and techniques, including the polarimetric whitening filter (PWF) and matched filter (PMF), for optimum processing of the data are developed. A complete target recognition system is developed, and performance results versus resolution and polarization are summarized. In part 2, super-resolution-processing algorithms applied to SAR data are described. For each algorithm, the improvement in ATR performance is quantified. Recognition performance results are presented for a 20-target classifier, and lessons learned from extensive SAR ATR evaluations conducted at MIT Lincoln Laboratory are summarized. Additional ATR topics presented include: target recognition performance comparisons using 1-D high range resolution (HRR) profile classifiers versus 2-D SAR classifiers; model-based classifier performance; combined polarimetric/super-resolution processing; low frequency foliage penetration (FOPEN) SAR target detection and discrimination.